Overview

Dataset statistics

Number of variables15
Number of observations16988
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.9 MiB
Average record size in memory120.0 B

Variable types

Numeric13
Categorical2

Alerts

TIME is highly correlated with S and 13 other fieldsHigh correlation
S is highly correlated with TIME and 13 other fieldsHigh correlation
T1 is highly correlated with TIME and 13 other fieldsHigh correlation
T3 is highly correlated with TIME and 12 other fieldsHigh correlation
T4 is highly correlated with TIME and 13 other fieldsHigh correlation
T5 is highly correlated with TIME and 13 other fieldsHigh correlation
T6 is highly correlated with TIME and 13 other fieldsHigh correlation
T7 is highly correlated with TIME and 13 other fieldsHigh correlation
T9 is highly correlated with TIME and 13 other fieldsHigh correlation
T10 is highly correlated with TIME and 12 other fieldsHigh correlation
T11 is highly correlated with TIME and 13 other fieldsHigh correlation
T12 is highly correlated with TIME and 13 other fieldsHigh correlation
Z is highly correlated with TIME and 13 other fieldsHigh correlation
T2 is highly correlated with TIME and 13 other fieldsHigh correlation
T8 is highly correlated with TIME and 13 other fieldsHigh correlation
TIME is uniformly distributed Uniform
TIME has unique values Unique
S has 7630 (44.9%) zeros Zeros
Z has 322 (1.9%) zeros Zeros

Reproduction

Analysis started2022-11-11 03:28:08.825859
Analysis finished2022-11-11 03:28:18.557753
Duration9.73 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

TIME
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct16988
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean707.875
Minimum0.08333333333
Maximum1415.666667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size132.8 KiB
2022-11-11T11:28:18.649521image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.08333333333
5-th percentile70.8625
Q1353.9791667
median707.875
Q31061.770833
95-th percentile1344.8875
Maximum1415.666667
Range1415.583333
Interquartile range (IQR)707.7916667

Descriptive statistics

Standard deviation408.6797935
Coefficient of variation (CV)0.5773332771
Kurtosis-1.2
Mean707.875
Median Absolute Deviation (MAD)353.9166667
Skewness8.160123072 × 10-16
Sum12025380.5
Variance167019.1736
MonotonicityStrictly increasing
2022-11-11T11:28:18.705366image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.083333333331
 
< 0.1%
944.33333331
 
< 0.1%
943.16666671
 
< 0.1%
943.251
 
< 0.1%
943.33333331
 
< 0.1%
943.41666671
 
< 0.1%
943.51
 
< 0.1%
943.58333331
 
< 0.1%
943.66666671
 
< 0.1%
943.751
 
< 0.1%
Other values (16978)16978
99.9%
ValueCountFrequency (%)
0.083333333331
< 0.1%
0.16666666671
< 0.1%
0.251
< 0.1%
0.33333333331
< 0.1%
0.41666666671
< 0.1%
0.51
< 0.1%
0.58333333331
< 0.1%
0.66666666671
< 0.1%
0.751
< 0.1%
0.83333333331
< 0.1%
ValueCountFrequency (%)
1415.6666671
< 0.1%
1415.5833331
< 0.1%
1415.51
< 0.1%
1415.4166671
< 0.1%
1415.3333331
< 0.1%
1415.251
< 0.1%
1415.1666671
< 0.1%
1415.0833331
< 0.1%
14151
< 0.1%
1414.9166671
< 0.1%

S
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct32
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6268.098893
Minimum0
Maximum20001
Zeros7630
Zeros (%)44.9%
Negative0
Negative (%)0.0%
Memory size132.8 KiB
2022-11-11T11:28:18.763866image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3999
Q313999
95-th percentile18000
Maximum20001
Range20001
Interquartile range (IQR)13999

Descriptive statistics

Standard deviation6911.825929
Coefficient of variation (CV)1.102698928
Kurtosis-1.189222485
Mean6268.098893
Median Absolute Deviation (MAD)3999
Skewness0.5794611252
Sum106482464
Variance47773337.67
MonotonicityNot monotonic
2022-11-11T11:28:18.815454image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
07630
44.9%
159991437
 
8.5%
140001247
 
7.3%
80001113
 
6.6%
12001719
 
4.2%
6000719
 
4.2%
9998718
 
4.2%
18000707
 
4.2%
3999678
 
4.0%
20001564
 
3.3%
Other values (22)1456
 
8.6%
ValueCountFrequency (%)
07630
44.9%
221
 
< 0.1%
1581
 
< 0.1%
14361
 
< 0.1%
1998164
 
1.0%
2000555
 
3.3%
22521
 
< 0.1%
3999678
 
4.0%
400141
 
0.2%
49001
 
< 0.1%
ValueCountFrequency (%)
20001564
 
3.3%
19999154
 
0.9%
199921
 
< 0.1%
18000707
4.2%
1799812
 
0.1%
159991437
8.5%
159931
 
< 0.1%
154351
 
< 0.1%
140001247
7.3%
13999190
 
1.1%

T1
Real number (ℝ≥0)

HIGH CORRELATION

Distinct51
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.36561396
Minimum24.7
Maximum27.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size132.8 KiB
2022-11-11T11:28:18.873628image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum24.7
5-th percentile24.7
Q124.8
median25
Q325.8
95-th percentile26.8
Maximum27.2
Range2.5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.6905189655
Coefficient of variation (CV)0.02722263954
Kurtosis0.06398873624
Mean25.36561396
Median Absolute Deviation (MAD)0.3
Skewness1.066192426
Sum430911.05
Variance0.4768164418
MonotonicityNot monotonic
2022-11-11T11:28:18.931637image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.83566
21.0%
24.92310
13.6%
24.71605
9.4%
251114
 
6.6%
25.1952
 
5.6%
25.5883
 
5.2%
26.4793
 
4.7%
26.1752
 
4.4%
25.8560
 
3.3%
27.2533
 
3.1%
Other values (41)3920
23.1%
ValueCountFrequency (%)
24.71605
9.4%
24.751
 
< 0.1%
24.83566
21.0%
24.8520
 
0.1%
24.92310
13.6%
24.9524
 
0.1%
251114
 
6.6%
25.0522
 
0.1%
25.1952
 
5.6%
25.1520
 
0.1%
ValueCountFrequency (%)
27.2533
3.1%
27.152
 
< 0.1%
27.179
 
0.5%
27.052
 
< 0.1%
2722
 
0.1%
26.951
 
< 0.1%
26.926
 
0.2%
26.851
 
< 0.1%
26.8249
1.5%
26.754
 
< 0.1%

T2
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size132.8 KiB
24.8
7118 
24.7
5043 
24.9
3429 
24.6
751 
24.5
 
647

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters67952
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row24.9
2nd row24.9
3rd row24.9
4th row24.9
5th row24.9

Common Values

ValueCountFrequency (%)
24.87118
41.9%
24.75043
29.7%
24.93429
20.2%
24.6751
 
4.4%
24.5647
 
3.8%

Length

2022-11-11T11:28:18.984461image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-11T11:28:19.035289image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
24.87118
41.9%
24.75043
29.7%
24.93429
20.2%
24.6751
 
4.4%
24.5647
 
3.8%

Most occurring characters

ValueCountFrequency (%)
216988
25.0%
416988
25.0%
.16988
25.0%
87118
10.5%
75043
 
7.4%
93429
 
5.0%
6751
 
1.1%
5647
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number50964
75.0%
Other Punctuation16988
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
216988
33.3%
416988
33.3%
87118
14.0%
75043
 
9.9%
93429
 
6.7%
6751
 
1.5%
5647
 
1.3%
Other Punctuation
ValueCountFrequency (%)
.16988
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common67952
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
216988
25.0%
416988
25.0%
.16988
25.0%
87118
10.5%
75043
 
7.4%
93429
 
5.0%
6751
 
1.1%
5647
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII67952
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
216988
25.0%
416988
25.0%
.16988
25.0%
87118
10.5%
75043
 
7.4%
93429
 
5.0%
6751
 
1.1%
5647
 
1.0%

T3
Real number (ℝ≥0)

HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.7371733
Minimum24.4
Maximum24.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size132.8 KiB
2022-11-11T11:28:19.077019image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum24.4
5-th percentile24.5
Q124.7
median24.8
Q324.8
95-th percentile24.8
Maximum24.9
Range0.5
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.1098855112
Coefficient of variation (CV)0.004442120764
Kurtosis1.930323989
Mean24.7371733
Median Absolute Deviation (MAD)0
Skewness-1.509640864
Sum420235.1
Variance0.01207482556
MonotonicityNot monotonic
2022-11-11T11:28:19.117575image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
24.89808
57.7%
24.73720
 
21.9%
24.61413
 
8.3%
24.4690
 
4.1%
24.5681
 
4.0%
24.9676
 
4.0%
ValueCountFrequency (%)
24.4690
 
4.1%
24.5681
 
4.0%
24.61413
 
8.3%
24.73720
 
21.9%
24.89808
57.7%
24.9676
 
4.0%
ValueCountFrequency (%)
24.9676
 
4.0%
24.89808
57.7%
24.73720
 
21.9%
24.61413
 
8.3%
24.5681
 
4.0%
24.4690
 
4.1%

T4
Real number (ℝ≥0)

HIGH CORRELATION

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.08837415
Minimum24.8
Maximum25.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size132.8 KiB
2022-11-11T11:28:19.158440image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum24.8
5-th percentile24.9
Q125
median25
Q325.2
95-th percentile25.4
Maximum25.6
Range0.8
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.1697001282
Coefficient of variation (CV)0.006764094285
Kurtosis-0.04614805649
Mean25.08837415
Median Absolute Deviation (MAD)0.1
Skewness0.6553172159
Sum426201.3
Variance0.02879813351
MonotonicityNot monotonic
2022-11-11T11:28:19.200118image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
256248
36.8%
25.22782
16.4%
24.92212
 
13.0%
25.11863
 
11.0%
25.31725
 
10.2%
25.4889
 
5.2%
24.8778
 
4.6%
25.5323
 
1.9%
25.6168
 
1.0%
ValueCountFrequency (%)
24.8778
 
4.6%
24.92212
 
13.0%
256248
36.8%
25.11863
 
11.0%
25.22782
16.4%
25.31725
 
10.2%
25.4889
 
5.2%
25.5323
 
1.9%
25.6168
 
1.0%
ValueCountFrequency (%)
25.6168
 
1.0%
25.5323
 
1.9%
25.4889
 
5.2%
25.31725
 
10.2%
25.22782
16.4%
25.11863
 
11.0%
256248
36.8%
24.92212
 
13.0%
24.8778
 
4.6%

T5
Real number (ℝ≥0)

HIGH CORRELATION

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.22229221
Minimum24.9
Maximum25.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size132.8 KiB
2022-11-11T11:28:19.242975image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum24.9
5-th percentile24.9
Q125
median25.3
Q325.4
95-th percentile25.4
Maximum25.5
Range0.6
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.1953826123
Coefficient of variation (CV)0.00774642569
Kurtosis-1.242591908
Mean25.22229221
Median Absolute Deviation (MAD)0.1
Skewness-0.4440682188
Sum428476.3
Variance0.03817436519
MonotonicityNot monotonic
2022-11-11T11:28:19.280145image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
25.46276
36.9%
24.92566
15.1%
25.22493
 
14.7%
251872
 
11.0%
25.31673
 
9.8%
25.11330
 
7.8%
25.5778
 
4.6%
ValueCountFrequency (%)
24.92566
15.1%
251872
 
11.0%
25.11330
 
7.8%
25.22493
 
14.7%
25.31673
 
9.8%
25.46276
36.9%
25.5778
 
4.6%
ValueCountFrequency (%)
25.5778
 
4.6%
25.46276
36.9%
25.31673
 
9.8%
25.22493
 
14.7%
25.11330
 
7.8%
251872
 
11.0%
24.92566
15.1%

T6
Real number (ℝ≥0)

HIGH CORRELATION

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.17607723
Minimum24.9
Maximum25.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size132.8 KiB
2022-11-11T11:28:19.322273image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum24.9
5-th percentile24.9
Q125
median25.2
Q325.3
95-th percentile25.7
Maximum25.8
Range0.9
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.2416313187
Coefficient of variation (CV)0.009597655602
Kurtosis0.2515666331
Mean25.17607723
Median Absolute Deviation (MAD)0.2
Skewness0.9228241772
Sum427691.2
Variance0.05838569416
MonotonicityNot monotonic
2022-11-11T11:28:19.360534image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
25.24747
27.9%
24.93691
21.7%
252282
13.4%
25.11803
 
10.6%
25.31667
 
9.8%
25.71384
 
8.1%
25.4624
 
3.7%
25.8322
 
1.9%
25.5263
 
1.5%
25.6205
 
1.2%
ValueCountFrequency (%)
24.93691
21.7%
252282
13.4%
25.11803
 
10.6%
25.24747
27.9%
25.31667
 
9.8%
25.4624
 
3.7%
25.5263
 
1.5%
25.6205
 
1.2%
25.71384
 
8.1%
25.8322
 
1.9%
ValueCountFrequency (%)
25.8322
 
1.9%
25.71384
 
8.1%
25.6205
 
1.2%
25.5263
 
1.5%
25.4624
 
3.7%
25.31667
 
9.8%
25.24747
27.9%
25.11803
 
10.6%
252282
13.4%
24.93691
21.7%

T7
Real number (ℝ≥0)

HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.69091712
Minimum24.3
Maximum24.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size132.8 KiB
2022-11-11T11:28:19.398406image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum24.3
5-th percentile24.4
Q124.6
median24.7
Q324.8
95-th percentile24.8
Maximum24.8
Range0.5
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.1356699702
Coefficient of variation (CV)0.00549473191
Kurtosis0.8808907914
Mean24.69091712
Median Absolute Deviation (MAD)0.1
Skewness-1.276973173
Sum419449.3
Variance0.01840634081
MonotonicityNot monotonic
2022-11-11T11:28:19.440265image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
24.87899
46.5%
24.74146
24.4%
24.62353
 
13.9%
24.51306
 
7.7%
24.4659
 
3.9%
24.3625
 
3.7%
ValueCountFrequency (%)
24.3625
 
3.7%
24.4659
 
3.9%
24.51306
 
7.7%
24.62353
 
13.9%
24.74146
24.4%
24.87899
46.5%
ValueCountFrequency (%)
24.87899
46.5%
24.74146
24.4%
24.62353
 
13.9%
24.51306
 
7.7%
24.4659
 
3.9%
24.3625
 
3.7%

T8
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size132.8 KiB
24.8
11730 
24.7
3451 
24.9
1807 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters67952
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row24.8
2nd row24.8
3rd row24.8
4th row24.8
5th row24.8

Common Values

ValueCountFrequency (%)
24.811730
69.0%
24.73451
 
20.3%
24.91807
 
10.6%

Length

2022-11-11T11:28:19.486192image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-11T11:28:19.533978image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
24.811730
69.0%
24.73451
 
20.3%
24.91807
 
10.6%

Most occurring characters

ValueCountFrequency (%)
216988
25.0%
416988
25.0%
.16988
25.0%
811730
17.3%
73451
 
5.1%
91807
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number50964
75.0%
Other Punctuation16988
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
216988
33.3%
416988
33.3%
811730
23.0%
73451
 
6.8%
91807
 
3.5%
Other Punctuation
ValueCountFrequency (%)
.16988
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common67952
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
216988
25.0%
416988
25.0%
.16988
25.0%
811730
17.3%
73451
 
5.1%
91807
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII67952
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
216988
25.0%
416988
25.0%
.16988
25.0%
811730
17.3%
73451
 
5.1%
91807
 
2.7%

T9
Real number (ℝ≥0)

HIGH CORRELATION

Distinct89
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.73510419
Minimum25
Maximum29.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size132.8 KiB
2022-11-11T11:28:19.584806image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile25.7
Q127.3
median27.9
Q328.5
95-th percentile29
Maximum29.4
Range4.4
Interquartile range (IQR)1.2

Descriptive statistics

Standard deviation0.9607090402
Coefficient of variation (CV)0.03463873918
Kurtosis0.05215791794
Mean27.73510419
Median Absolute Deviation (MAD)0.6
Skewness-0.8453892349
Sum471163.95
Variance0.92296186
MonotonicityNot monotonic
2022-11-11T11:28:19.701407image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27.71044
 
6.1%
28.6880
 
5.2%
28.5856
 
5.0%
28.3848
 
5.0%
28.2795
 
4.7%
28.4735
 
4.3%
27.9719
 
4.2%
27.5716
 
4.2%
27.4712
 
4.2%
27.2706
 
4.2%
Other values (79)8977
52.8%
ValueCountFrequency (%)
2524
 
0.1%
25.052
 
< 0.1%
25.158
0.3%
25.152
 
< 0.1%
25.231
 
0.2%
25.252
 
< 0.1%
25.353
0.3%
25.354
 
< 0.1%
25.4125
0.7%
25.454
 
< 0.1%
ValueCountFrequency (%)
29.455
 
0.3%
29.356
 
< 0.1%
29.3160
0.9%
29.2514
 
0.1%
29.2195
1.1%
29.1510
 
0.1%
29.1205
1.2%
29.0516
 
0.1%
29255
1.5%
28.9514
 
0.1%

T10
Real number (ℝ≥0)

HIGH CORRELATION

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.97548858
Minimum24.6
Maximum25.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size132.8 KiB
2022-11-11T11:28:19.748439image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum24.6
5-th percentile24.7
Q124.9
median25
Q325.1
95-th percentile25.2
Maximum25.3
Range0.7
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.1495570361
Coefficient of variation (CV)0.005988152569
Kurtosis-0.7069813778
Mean24.97548858
Median Absolute Deviation (MAD)0.1
Skewness-0.1508716333
Sum424283.6
Variance0.02236730705
MonotonicityNot monotonic
2022-11-11T11:28:19.786494image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
24.94506
26.5%
25.13451
20.3%
253046
17.9%
25.22494
14.7%
24.82413
14.2%
24.7796
 
4.7%
24.6247
 
1.5%
25.335
 
0.2%
ValueCountFrequency (%)
24.6247
 
1.5%
24.7796
 
4.7%
24.82413
14.2%
24.94506
26.5%
253046
17.9%
25.13451
20.3%
25.22494
14.7%
25.335
 
0.2%
ValueCountFrequency (%)
25.335
 
0.2%
25.22494
14.7%
25.13451
20.3%
253046
17.9%
24.94506
26.5%
24.82413
14.2%
24.7796
 
4.7%
24.6247
 
1.5%

T11
Real number (ℝ≥0)

HIGH CORRELATION

Distinct18
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.98090417
Minimum24.1
Maximum25.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size132.8 KiB
2022-11-11T11:28:19.830352image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum24.1
5-th percentile24.3
Q124.7
median25
Q325.3
95-th percentile25.6
Maximum25.8
Range1.7
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation0.3817205283
Coefficient of variation (CV)0.01528049288
Kurtosis-0.6838428283
Mean24.98090417
Median Absolute Deviation (MAD)0.3
Skewness-0.2066845082
Sum424375.6
Variance0.1457105617
MonotonicityNot monotonic
2022-11-11T11:28:19.872677image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
25.31866
11.0%
24.91792
10.5%
251755
10.3%
25.11608
9.5%
25.21349
7.9%
24.81184
 
7.0%
24.51134
 
6.7%
24.41093
 
6.4%
25.51024
 
6.0%
25.4979
 
5.8%
Other values (8)3204
18.9%
ValueCountFrequency (%)
24.1198
 
1.2%
24.2214
 
1.3%
24.3444
 
2.6%
24.41093
6.4%
24.51134
6.7%
24.6678
 
4.0%
24.7784
4.6%
24.81184
7.0%
24.91792
10.5%
251755
10.3%
ValueCountFrequency (%)
25.8179
 
1.1%
25.7196
 
1.2%
25.6511
 
3.0%
25.51024
6.0%
25.4979
5.8%
25.31866
11.0%
25.21349
7.9%
25.11608
9.5%
251755
10.3%
24.91792
10.5%

T12
Real number (ℝ≥0)

HIGH CORRELATION

Distinct17
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.96908406
Minimum24
Maximum25.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size132.8 KiB
2022-11-11T11:28:19.918489image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum24
5-th percentile24.3
Q124.8
median25
Q325.2
95-th percentile25.4
Maximum25.6
Range1.6
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.3399329653
Coefficient of variation (CV)0.01361415439
Kurtosis-0.05446894489
Mean24.96908406
Median Absolute Deviation (MAD)0.2
Skewness-0.7410690888
Sum424174.8
Variance0.1155544209
MonotonicityNot monotonic
2022-11-11T11:28:19.961559image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
252341
13.8%
25.22328
13.7%
25.12293
13.5%
25.31980
11.7%
24.91887
11.1%
24.41111
6.5%
24.81099
6.5%
25.4736
 
4.3%
24.6557
 
3.3%
25.5557
 
3.3%
Other values (7)2099
12.4%
ValueCountFrequency (%)
2433
 
0.2%
24.1287
 
1.7%
24.2366
 
2.2%
24.3360
 
2.1%
24.41111
6.5%
24.5392
 
2.3%
24.6557
 
3.3%
24.7504
 
3.0%
24.81099
6.5%
24.91887
11.1%
ValueCountFrequency (%)
25.6157
 
0.9%
25.5557
 
3.3%
25.4736
 
4.3%
25.31980
11.7%
25.22328
13.7%
25.12293
13.5%
252341
13.8%
24.91887
11.1%
24.81099
6.5%
24.7504
 
3.0%

Z
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct124
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.21880822
Minimum-2.438
Maximum63.375
Zeros322
Zeros (%)1.9%
Negative1076
Negative (%)6.3%
Memory size132.8 KiB
2022-11-11T11:28:20.016173image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-2.438
5-th percentile-1.219
Q112.187
median25.593
Q336.562
95-th percentile53.625
Maximum63.375
Range65.813
Interquartile range (IQR)24.375

Descriptive statistics

Standard deviation16.2047189
Coefficient of variation (CV)0.6425648175
Kurtosis-0.6124670771
Mean25.21880822
Median Absolute Deviation (MAD)12.187
Skewness0.1671688545
Sum428417.114
Variance262.5929146
MonotonicityNot monotonic
2022-11-11T11:28:20.070651image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.219967
 
5.7%
26.812694
 
4.1%
25.593639
 
3.8%
28.031569
 
3.3%
24.375535
 
3.1%
39470
 
2.8%
1.218429
 
2.5%
29.25412
 
2.4%
23.156409
 
2.4%
30.468374
 
2.2%
Other values (114)11490
67.6%
ValueCountFrequency (%)
-2.43815
 
0.1%
-1.828549
 
0.3%
-1.219967
5.7%
-0.609545
 
0.3%
0322
 
1.9%
0.60940
 
0.2%
1.218429
2.5%
1.827533
 
0.2%
2.437134
 
0.8%
3.046533
 
0.2%
ValueCountFrequency (%)
63.3753
 
< 0.1%
62.76553
 
< 0.1%
62.156220
1.3%
61.5465104
0.6%
60.937176
1.0%
60.327541
 
0.2%
59.71812
 
0.1%
59.1093
 
< 0.1%
58.516
 
0.1%
57.89058
 
< 0.1%

Interactions

2022-11-11T11:28:17.728324image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:09.525532image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:10.183922image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:10.917023image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:11.542931image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:12.248401image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:12.926898image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:13.644408image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:14.269110image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:14.972925image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:15.595370image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:16.332142image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:16.985028image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:17.778120image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:09.577357image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:10.236000image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:10.966474image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:11.593760image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:12.301413image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:12.977924image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:13.692193image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:14.319461image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:15.020810image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:15.647195image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:16.382380image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:17.037886image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:17.830164image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:09.630087image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:10.289385image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:11.017352image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:11.645590image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:12.355799image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:13.030629image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:13.743979image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:14.370289image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:15.071029image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:15.701014image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:16.435169image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:17.092706image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:17.877401image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:09.677891image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:10.338240image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:11.062330image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:11.692432image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:12.405085image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:13.078468image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:13.788453image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:14.416242image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:15.115878image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:15.749294image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:16.482087image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:17.141946image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:17.926183image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:09.727724image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:10.389545image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:11.109396image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:11.740138image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:12.455908image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:13.127203image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:13.835850image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:14.464751image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:15.163764image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:15.799127image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:16.530948image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:17.192739image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:17.978586image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:09.781542image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:10.443363image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:11.159937image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:11.792212image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:12.511154image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:13.180156image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:13.886678image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:14.516813image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:15.214318image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:15.853723image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:16.584732image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:17.247610image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:18.028007image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:09.832371image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:10.495971image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:11.209047image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:11.841553image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:12.563976image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:13.229988image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:13.934372image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:14.565714image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:15.262501image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:15.905760image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:16.634564image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:17.365537image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:18.073817image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:09.879905image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:10.544325image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:11.253896image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:11.888396image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:12.613872image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:13.277740image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:13.980157image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:14.612556image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:15.307396image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:15.953648image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:16.682198image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:17.414380image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:18.121646image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:09.929855image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:10.595153image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:11.300941image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:11.937283image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:12.664607image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:13.326539image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:14.027184image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:14.659451image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:15.354025image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:16.077334image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:16.731033image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:17.465217image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:18.168645image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:09.976697image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:10.643038image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:11.346243image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:11.982445image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:12.713672image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:13.438075image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:14.071751image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:14.771512image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:15.398936image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:16.125831image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:16.778181image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:17.514634image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:18.219441image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:10.029519image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:10.696620image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:11.395760image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:12.033274image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:12.767336image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:13.490612image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:14.122584image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:14.822662image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:15.448714image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:16.178025image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:16.830076image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:17.568395image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:18.269273image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:10.080868image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:10.813226image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:11.444110image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:12.148619image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:12.819211image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:13.541135image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:14.170846image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:14.871919image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:15.496758image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:16.228419image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:16.882897image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:17.621217image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:18.320937image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:10.134313image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:10.867105image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:11.495092image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:12.200562image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:12.874413image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:13.594790image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:14.222128image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:14.924089image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:15.547531image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:16.282205image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:16.935773image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:28:17.676032image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-11-11T11:28:20.126463image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-11-11T11:28:20.200215image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-11T11:28:20.276956image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-11T11:28:20.354528image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-11T11:28:20.421681image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-11-11T11:28:20.474277image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-11T11:28:18.402195image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-11T11:28:18.513901image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

TIMEST1T2T3T4T5T6T7T8T9T10T11T12Z
00.083333024.724.924.925.025.525.724.824.827.925.125.225.20.0
10.166667024.724.924.925.025.525.724.824.827.925.125.225.10.0
20.250000024.724.924.925.025.525.724.824.827.925.125.225.10.0
30.333333024.724.924.925.025.525.724.824.827.925.125.225.10.0
40.416667024.724.924.925.025.525.724.824.827.925.125.225.20.0
50.500000024.724.924.925.025.525.724.824.827.925.125.225.10.0
60.583333024.724.924.925.025.525.724.824.827.925.125.225.10.0
70.666667024.724.924.925.025.525.724.824.827.925.125.225.20.0
80.750000024.724.924.925.025.525.724.824.827.925.125.225.10.0
90.833333024.724.924.925.025.525.724.824.827.925.125.225.10.0

Last rows

TIMEST1T2T3T4T5T6T7T8T9T10T11T12Z
169781414.916667024.824.824.825.025.425.424.824.827.024.924.424.61.218
169791415.000000024.824.824.825.025.425.424.824.827.024.824.424.61.218
169801415.083333024.824.824.825.025.425.424.824.827.024.924.424.61.218
169811415.166667024.824.824.825.025.425.424.824.827.024.924.424.61.218
169821415.250000024.824.824.825.025.425.424.824.827.024.924.424.61.218
169831415.333333024.824.824.825.025.425.424.824.827.024.924.424.61.218
169841415.416667024.824.824.825.025.425.424.824.827.024.824.424.61.218
169851415.500000024.824.824.825.025.425.424.824.827.024.824.424.61.218
169861415.583333024.824.824.825.025.425.424.824.827.024.824.424.61.218
169871415.666667024.824.824.825.025.425.424.824.827.024.824.424.61.218